Online detection system and method with function of correcting first printed matter

Abstract

The invention relates to an on-line detection system and method with a printed matter first sheet proofing function, and aims to improve the first detection efficiency and accuracy of a printing enterprise. The method comprises the following steps: collecting a first printed matter image, and simultaneously carrying out online modeling and online proofing according to the image; the first product image is captured in real time through the high-resolution image acquisition device, and the printing plate electronic file and the first printed matter image are accurately calibrated and registered by using an image processing technology, so that the geometric consistency between the printing plate electronic file and the first printed matter image is ensured. And then, comparing the structural elements one by one by the system, finding out difference points, screening and evaluating, and generating a detailed online proof report. If the result is qualified, continuous printing production is carried out by taking the created online model as a standard; otherwise, checking the problem, and repeating calibration, registration and comparison operations until the product is qualified. According to the method, the first detection time is remarkably shortened, manual intervention is reduced, the detection precision and reliability are improved, the rejection rate is reduced, and the customer satisfaction and the market competitiveness are enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of printing quality detection, and in particular to an online detection system and method with a first proofreading function for printed products. Background Art

[0002] Printing companies usually need to proofread the first print (i.e., first inspection) before batch printing to ensure the correctness of the content of the printing layout file and avoid batch scrapping due to layout errors or information loss. In the prior art, the first inspection is mainly carried out using offline inspection equipment or manual proofing. These methods have the following problems: The traditional manual first inspection method requires the collaboration of multiple people, the process is complicated, time-consuming, and easily affected by subjective judgment, and there is a risk of missed inspections or wrong inspections. The offline inspection method requires the first print to be sent to the inspection equipment to complete the image collection and inspection. The process involves multiple positions and operating steps, which not only increases the time cost, but also may increase the probability of error due to multiple handling and operations. In addition, the waiting time for offline inspection results often causes the printing machine to be idle, reducing production efficiency. Summary of the invention

[0003] In view of one or more of the problems existing in the prior art, the first aspect of the present application provides an online detection method with a first proofreading function for printed matter, comprising: Capture the first print image while printing the first print; Online modeling based on first print images; Online proofing based on first print images; When the online proofing results are qualified, the online modeling results are used as the testing standard for continuous printing production; Among them, online modeling based on the first print image and online proofing based on the first print image are carried out simultaneously; Online proofing based on first print images includes: Preprocess the electronic file of the printing plate to obtain a comparison image of the electronic file with a set resolution; Calibrate the first print image against the electronic file reference image; Register the electronic file reference image and the calibrated first print image; The image structure of the first printed product after registration is compared and evaluated with the reference image of the electronic file, and the online proofreading result is output.

[0004] Preferably, preprocessing the electronic file of the printing plate to obtain an electronic file comparison image of a set resolution includes: Converting the electronic file of the printing plate into a first image of a set resolution; Segmenting the first image into pattern and background, and performing blur processing; The pattern texture of the first image is extracted and merged according to the three color channels of RGB, and a texture mask image is generated through a threshold value to obtain an electronic file comparison image of a set resolution.

[0005] Preferably, calibrating the first printed product image according to the electronic document reference image comprises: Adjust the resolution of the first printed image to make it consistent with the resolution of the electronic file reference image with the set resolution; Performing geometric correction on the first print image to restore local deformation of the first print image; Perform color correction on the first print image to further reduce the difference between the first print image and the electronic file comparison image; The first printed image is filtered to make the blur degree of the first printed image nearly consistent with the blur degree of the electronic file comparison image.

[0006] Preferably, geometrically correcting the first printed product image specifically includes: A plurality of coarse corner points are obtained on the first printed image by using a corner detection algorithm; fine corner points are screened out from the coarse corner points by using a clustering algorithm; a similarity comparison is performed between the fine corner points and the electronic file control image in the corresponding area based on the fine corner points; and the position, rotation and scaling of the first printed image are adjusted according to the comparison results to correct local deformation.

[0007] Preferably, registering the electronic document reference image and the calibrated first printed product image comprises: Detect and extract significant feature points in electronic document comparison images; Select the positioning kernel from the feature points; Determine the best matching point corresponding to each positioning kernel in the first print image to establish a position correspondence; Mapping of multiple areas between the electronic file of the printing plate and the first print image is achieved based on matching of a large number of positioning kernels; The local mapping relationship is weighted to obtain the accurate mapping relationship between each position of the images.

[0008] Preferably, comparing and evaluating the registered first printed product image structure with the electronic file reference image, and outputting the online proofing result includes: Based on the position correspondence after registration, the structural elements of the first printed image and the electronic file reference image are compared one by one to find out the differences; According to the preset evaluation method, the difference points are screened and the online proofreading results are generated.

[0009] Preferably, comparing and evaluating the registered first printed product image structure with the electronic file reference image, and outputting the online proofing result further includes: The online proofreading results are displayed through the human-computer interaction module. The operator reviews the online proofreading results. If the online proofreading results are unqualified, appropriate measures must be taken to correct them and then repeat the calibration, registration and comparison operations until they are qualified.

[0010] The second aspect of the present application provides an online detection system with a printed product first proofing function, which is used for online detection of printing production controlled by a printing control module, including: An image acquisition module, used for acquiring the first printed product image; An online modeling module, connected to the image acquisition module, and used for performing online modeling according to the first printed product image; A first proofing module, connected to the image acquisition module and the printing control module, for performing online proofing according to the first printed product image; A human-computer interaction module, connected to the first proofing module and the printing control module, for displaying the online proofing result and issuing a control instruction to the printing device according to the online proofing result; The online detection module is connected to the online modeling module and is used to detect the printed products produced by continuous printing by taking the online modeling results as the detection standard.

[0011] Preferably, the first proofing module comprises: A calibration unit, connected to the image acquisition module and the printing control module, for receiving the first printed product image and the printing plate electronic file, preprocessing the printing plate electronic file to obtain an electronic file comparison image of a set resolution, and calibrating the first printed product image according to the electronic file comparison image; A registration unit connected to the calibration unit, for combining the calibrated first printed product image and registering the electronic file comparison image with the calibrated first printed product image; The evaluation unit is connected to the registration unit and the human-computer interaction module respectively, and is used to compare and evaluate the image structure of the first printed product after registration with the electronic file comparison image, and output the online proofreading result to the human-computer interaction module.

[0012] One or more of the above embodiments have at least the following beneficial effects: First, the first inspection time is significantly shortened. Traditional manual first inspection or offline inspection methods usually take more than half an hour to complete the proof inspection of the first product. However, this invention can complete the entire process within 5 minutes by parallel processing of modeling and proofing tasks, combined with high-resolution image acquisition and real-time data analysis. This rapid response capability not only improves work efficiency, but also reduces production stagnation caused by waiting for the first inspection results.

[0013] Secondly, improve the detection accuracy and reliability. The online detection system uses image processing algorithms (such as SIFT, SURF, etc.) to extract, align and evaluate feature points, ensuring the consistency and accuracy between the first print image and the electronic file of the printing plate. Through precise alignment and difference point screening mechanism, the system can effectively identify subtle defects, avoiding omissions and misjudgments that may occur in manual inspection, thereby ensuring the quality consistency of each print.

[0014] Furthermore, it reduces labor costs and operational complexity. The system realizes the automation of the entire process from image acquisition to the final proofing result output, reducing the need for multi-position and multi-person collaboration. Operators only need to input simple instructions and review and confirm on the human-computer interaction module, which greatly simplifies the workflow, reduces the uncertainty caused by human factors, and also improves the safety and convenience of operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings: Figure 1 is a flow chart of an online detection method with a first proofing function of printed matter provided by an exemplary embodiment of the present application; Figure 2 It is a schematic diagram of the clustering results of the precise corner points during the geometric correction in the calibration of the online detection method with the first proofing function of the printed product provided by an exemplary embodiment of the present application; Figure 3 It is a structural schematic diagram of an online detection system with a first proofreading function of printed matter provided by an exemplary embodiment of the present application; Figure 4 It is a structural schematic diagram of a first proofing module of an online detection system with a first proofing function for printed products provided by an exemplary embodiment of the present application.

[0016] Reference numerals: 100. Printing control module; 200. Image acquisition module; 300. Online modeling module; 400. First proofing module; 410. Calibration unit; 420. Registration unit; 430. Evaluation unit; 500. Human-computer interaction module; 600. Online monitoring module. DETAILED DESCRIPTION

[0017] Embodiments of the present application will be described in detail below, examples of which are shown in the accompanying drawings, and the components of the embodiments of the present application generally described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the application claimed for protection, but merely represents selected embodiments of the present application.

[0018] Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of this application.

[0019] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0020] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0021] The following will be combined Figures 1 to 4 The technical solution of the present application is described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0022] Figure 1 It is a flow chart of an online detection method with a first proofreading function of a printed product provided by an exemplary embodiment of the present application.

[0023] See also Figure 1 In a first aspect, the present application provides an online detection method with a first proofreading function for printed matter, comprising the following steps: S100, collecting the image of the first printed product while printing the first printed product.

[0024] When the printing equipment prints the first product according to the electronic file of the printing plate (such as PDF file, electronic design drawing, etc.), the image acquisition module immediately completes the capture of the sample to ensure that the image quality is sufficient for subsequent processing and analysis. This step is the basis for realizing the first proof online and provides raw data for subsequent modeling and proofing.

[0025] The image acquisition module should have high resolution and fast response capabilities, and be able to obtain clear image data without affecting the printing speed.

[0026] In some specific examples, the electronic file of the printing plate can be transferred to the printing control module of the printing device via the enterprise's internal network (LAN) or a dedicated file transfer protocol (such as FTP, HTTP, SMB) to facilitate the printing operation.

[0027] In some specific examples, the image acquisition module can use high-performance cameras, such as industrial-grade high-speed cameras, high-resolution HD cameras, etc., supporting multiple image formats (such as JPEG, TIFF), to ensure the capture of high-quality first product images. High-resolution images provide a reliable basis for subsequent comparisons and reduce the possibility of misjudgment due to poor image quality.

[0028] In some specific examples, there is a synchronous trigger mechanism between the image acquisition module and the printing device, that is, through the linkage control of the image acquisition module and the printing device, the image acquisition module is automatically triggered to shoot the moment the first printed product leaves the printing device, without the need for human intervention, and a seamless connection from printing to image acquisition is achieved, shortening the time of the entire first inspection process.

[0029] S200, online modeling based on the first print image.

[0030] After acquiring the first printed image, the image acquisition module transmits it to the online modeling module, which can analyze the key feature points in the image and record this information to create an accurate inspection template for subsequent print quality inspection.

[0031] The inspection template contains the structural features of the first printed product (such as text, graphics, color, etc.) and serves as a reference standard for subsequent production.

[0032] In some specific examples, the online modeling module can use image processing techniques, such as edge detection and corner point recognition, to accurately locate text, graphics and other important elements in the image; then, using computer vision and machine learning algorithms, these features are digitally represented to build a high-precision detection template. For example, if a printed product contains a piece of text and a pattern, the system will extract the font, size, spacing of the text, and the edge information of the pattern to create the corresponding modeling data.

[0033] Online modeling can create an accurate digital model based on the actual printed image as a standard template for subsequent product testing. In this way, automated and standardized production can be achieved, manual operations and human errors can be reduced, and the controllability and stability of printing quality can be improved.

[0034] S200', online proofing based on first print image.

[0035] After acquiring the first print image, the image acquisition module transmits it to the first proofing module, which compares the acquired first print image with the electronic file of the printing plate and automatically performs online proofing. The first proofing module uses algorithms to detect various indicators in the first print image, including ink spots, text errors, color deviations, missing patterns, etc., to ensure that the print meets the predetermined standards. If the difference between the image and the electronic sample exceeds the allowable error range, the first proofing module will mark it as unqualified and provide specific defect information.

[0036] In some specific examples, the printing control module controls the transmission of the electronic plate file to the first proofing module, or transmits the electronic plate file from the file storage server to the first proofing module via a network protocol (such as FTP, HTTP, SMB, or a dedicated enterprise file transfer protocol).

[0037] In some specific examples, assuming that the printed product contains a text and a graphic, the first proof module will compare the captured printed image with the corresponding part in the standard electronic sample. If differences are found in the color of the graphic or the size of the font, the system will detect these differences through image processing algorithms and automatically mark them as "color deviation" or "font mismatch".

[0038] In some specific examples, step S200, online modeling based on the first printed image, and step S200', online proofing based on the first printed image, are performed simultaneously. By processing the modeling and proofing steps in parallel, the time of the overall first proofing process can be significantly reduced. Traditionally, modeling and proofing are usually performed linearly, with proof checking completed before modeling. Performing these two steps in parallel can synchronously start proofing inspection and online modeling immediately after the image is captured, significantly improving the processing speed of the first proof, avoiding waiting time in the production process, and improving production efficiency.

[0039] In some embodiments, step S200', performing online proofing according to the first printed product image, specifically comprises the following steps: S210', pre-processing the electronic file of the printing plate to obtain an electronic file comparison image of a set resolution.

[0040] The purpose of preprocessing the electronic file of the printing plate is to ensure that the electronic file image used for comparison has the same resolution as the first printed product image collected, so as to eliminate content misalignment and comparison errors caused by resolution differences.

[0041] In some embodiments, preprocessing the electronic file of the printing plate to obtain an electronic file comparison image of a set resolution includes: Convert the electronic plate file to the first image at a set resolution: Segmenting the first image into pattern and background, and performing blur processing; The pattern texture of the first image is extracted and merged according to the three color channels of RGB, and a texture mask image is generated through a threshold value to obtain an electronic file comparison image of a set resolution.

[0042] In some specific examples, the first proofing module can determine a suitable output resolution (e.g., 300dpi) as the set resolution based on the specific requirements of the printing equipment and the resolution of the first printed image; then, the first proofing module converts the original printing plate electronic file (usually in PDF or EPS format) into a first image of the set resolution.

[0043] In some specific examples, professional graphics processing software (such as Adobe Illustrator or CorelDRAW) can be used to convert the original plate electronic file (usually in PDF or EPS format) into a high-resolution raster image (first image). This process retains the vector information in the original file, ensuring that the converted image quality is not damaged.

[0044] In some specific examples, the purpose of segmenting the pattern and background of the first image and blurring it is to reduce local deformation and distortion through segmentation and blurring, and to improve the accuracy of subsequent feature extraction. The first proofing module can apply an image segmentation algorithm (such as GrabCut or a semantic segmentation model based on deep learning) to accurately separate the pattern (foreground) and background in the first image. This segmentation can effectively remove background interference, allowing subsequent processing to focus more on key areas. Afterwards, Gaussian blur processing with different parameters is applied to the segmented pattern part. By adjusting the blur radius, the internal details can be smoothed while maintaining the clarity of the pattern edge, reducing minor deformations that may affect the comparison.

[0045] In some specific examples, the first proof module uses different texture analysis algorithms (such as gray level co-occurrence matrix GLCM, local binary pattern LBP, etc.) according to the three color channels of red (R), green (G), and blue (B) to extract texture features from each color channel. These algorithms can capture the local structure and repetitive patterns in the image, providing rich information for subsequent comparison. Afterwards, the first proof module merges the extracted texture features according to the three RGB channels to generate a complete texture image. Then, by setting an appropriate threshold, a texture mask image is generated. This method can highlight the important texture areas in the image while suppressing noise and other irrelevant information. If necessary, the first proof module can also further optimize the generated texture mask image, such as applying morphological operations (dilation, erosion) to clean up noise points to ensure the integrity and accuracy of the mask. Finally, an electronic file comparison image of a set resolution is obtained.

[0046] Through the above detailed steps, the present application converts the electronic file of the printing plate into an electronic file comparison image and maintains the resolution and color consistency of the electronic file comparison image and the first print, thereby eliminating the comparison error caused by resolution difference and color deviation, and significantly improving the accuracy of detection. In addition, through pattern and background segmentation, blur processing and multi-channel texture extraction of the electronic file comparison image, the key features in the image are enhanced, making subsequent feature matching more reliable.

[0047] S220', calibrate the first printed product image according to the electronic file reference image.

[0048] The online inspection system with the function of first proofing printed products provided by this application is installed on the production equipment. The jitter of the product paper and the slight deformation of the image acquisition module during the image acquisition process will affect the quality of the first printed product image collected online, and the first proofing has high requirements for image quality. Therefore, calibrating the first printed product image collected online to ensure that it can meet the first inspection requirements for comparison with the electronic file reference image is a necessary prerequisite for the function.

[0049] In some embodiments, calibrating the first printed product image according to the electronic file reference image includes: Adjust the resolution of the first printed image to make it consistent with the resolution of the electronic document comparison image with the set resolution, ensuring that the first printed image and the electronic document comparison image have the same resolution to eliminate content misalignment and comparison errors caused by resolution differences; Perform geometric correction on the first print image, select coarse corner points on the first print image through an algorithm, and further screen out fine corner points as calibration base nodes; perform similarity comparison and correction and restoration with the electronic file reference image within the scope of each calibration base node to restore the local deformation of the first print image to the greatest extent; correct the geometric deformation of the first print image caused by scanning, shooting or other reasons to ensure its consistency in shape with the electronic file reference image.

[0050] Perform color correction on the first print image to further reduce the difference between the first print image and the electronic file comparison image; The first printed image is filtered to make the blur degree of the first printed image close to that of the electronic file comparison image, thereby reducing the comparison error caused by different blur degrees.

[0051] In some specific examples, the first proofing module uses the resolution of the known electronic file to compare the image, and uses the interpolation algorithm in the image processing software (such as Adobe Photoshop or GIMP) to adjust the first print image to the same resolution. Common interpolation methods include nearest neighbor, bilinear, bicubic, etc. The method that best maintains the quality of the original image is selected. When adjusting the resolution, pay special attention to maintaining the clarity of small text and patterns in the image to avoid blur or distortion caused by enlargement or reduction.

[0052] Figure 2 It is a schematic diagram of the clustering results of precise corner points during geometric correction in calibration in an online detection method with a first proofreading function of printed products provided by an exemplary embodiment of the present application.

[0053] In some specific examples, the first proofing module can use computer vision technology (such as Harris corner detection or FAST feature detection) to automatically identify multiple possible corner points on the first print image as preliminary positioning points (rough corner points) to calibrate the key positions of image geometric correction.

[0054] See also Figure 2 , 50 coarse corner points are obtained by searching the whole image through the FAST function, and each corner point meets the filtering requirements of the correlation score requirements and the difference between the distances. The K-means algorithm is further applied to reduce the coarse corner points to the most representative fine corner points through unsupervised learning clustering. Figure 2The large and medium-sized boxes represent the image area, and there are 20 selected precision corner points inside, that is, each hollow in the figure, and the boxes around it represent the calibration range of the point. These selected precision corner points will be used as basic nodes in the subsequent correction process. Finally, based on the selected precision corner points, the similarity of the local area is compared with the electronic file control image within the range of each calibration basic node. Indicators such as normalized cross correlation (NCC) and structural similarity (SSIM) are used to measure the similarity, and the position, rotation and scaling of the first print image are adjusted accordingly to restore local deformation to the greatest extent and reduce the difference in false detection of images during subsequent first inspection comparison and evaluation.

[0055] Among them, the FAST (Features from Accelerated Segment Test) function is an algorithm for corner detection. It identifies corners by quickly detecting changes in pixel values ​​in an image. Specifically, the FAST algorithm checks the continuous pixel segments around a pixel. If the brightness of these pixel segments is significantly different from that of the central pixel (that is, it meets a certain threshold condition), the pixel is determined to be a corner.

[0056] The K-means algorithm is an unsupervised learning clustering algorithm that is used to divide the data points in a data set into K clusters. The algorithm iteratively updates the centroid of the cluster and assigns the data point to the cluster to which the nearest centroid belongs.

[0057] In some specific examples, color correction is achieved using a suitable lookup table, such as an SRGB lookup table, which can standardize the color space to ensure that the color representation of the first printed image is consistent with the electronic file reference image.

[0058] In some specific examples, the filter processing is performed by using a guide map filter. The electronic sample file reference image is used as the guide reference image, and the first printed image after geometric correction and color correction is used as the object to be guided. This method can smooth the noise while retaining the edge details, ensuring that the blur level of the final output image is roughly consistent, meeting the next step of registration requirements.

[0059] Through the above detailed steps, this application eliminates the errors caused by differences in imaging conditions by strictly controlling resolution, geometric correction, color correction and filtering processing, and greatly improves the accuracy and reliability of the comparison between the first printed image and the electronic file comparison image.

[0060] S230', registering the electronic document reference image and the calibrated first printed product image.

[0061] After calibration, the first proof module registers the plate electronic file with the calibrated first print image. Registration refers to the precise alignment of the two for subsequent difference comparison. The purpose of registration is to ensure that the key elements in the print image can accurately correspond to the corresponding parts in the plate electronic file, providing an accurate basis for subsequent comparative evaluation.

[0062] In some embodiments, the registration process of the first proof module includes: automatically detecting and extracting significant feature points in the electronic file of the printing plate; from the large number of extracted feature points, through a series of screening rules (such as stability, repeatability, etc.), selecting the most representative and reliable points as "positioning kernels", these positioning kernels will become the key reference points for the subsequent establishment of mapping relationships; for each positioning kernel, find its corresponding best matching point in the first print image, so as to establish multiple position correspondences, this step can be achieved by nearest neighbor search, template matching or other similarity measurement methods; through the matching of a large number of positioning kernels, the mapping of multiple areas between the electronic file of the printing plate and the image of the first print can be achieved, and this mapping is not limited to a single feature point, but also includes the overall structure and shape of the area where these points are located; in order to improve the mapping accuracy, the first proof module will weight the local mapping relationship: specifically, according to the importance of each positioning kernel and the influence of its surrounding environment, different weight values ​​are assigned, and finally the accurate mapping relationship between each position between the images is obtained. This method can effectively reduce the overall deviation caused by the accumulation of local errors.

[0063] In some specific examples, these feature points may be corners of patterns, edges of text, intersections of graphics, etc. They are unique and stable locations in the image and are suitable as reference points for comparison.

[0064] In some specific examples, image processing algorithms (such as SIFT, SURF, or ORB) are used to identify and locate these feature points. These algorithms can effectively capture local invariant features in images and maintain good recognition results even in the presence of slight deformation or lighting changes.

[0065] By accurately registering the electronic file of the printing plate and the image of the first calibrated print, the present invention ensures the geometric consistency between the print and the design file, providing a reliable basis for subsequent image comparison and evaluation. The registration process involves multiple steps such as feature point detection and extraction, positioning kernel selection and matching, multi-region mapping, and weighted processing, aiming to maximize the accuracy and reliability of detection. This method not only optimizes work efficiency, but also brings significant quality improvement and technical advantages to printing companies.

[0066] S240', compare and evaluate the image structure of the first printed product after registration with the electronic file comparison image, and output the online proofing result.

[0067] After the registration is completed, the first proofing module compares and evaluates the first printed image after registration with the electronic file of the printing plate. The purpose of this step is to detect the differences between the two, screen and evaluate these differences according to the set standards, and output the online proofing results.

[0068] In some embodiments, the comparison and evaluation process of the first proof module includes: based on the position correspondence after registration, the first proof module will compare the image structure of the first print with the structural elements of the electronic file of the printing plate one by one, identify and mark any differences between the two images; according to the pre-set evaluation criteria (such as tolerance range, threshold, etc.), the first proof module will screen the found differences, and the screening is intended to distinguish between real problems and small errors that can be ignored; for the screened differences, the first proof module will prioritize them according to their severity to ensure that the most important problems are paid attention to and handled first. Finally, the online proof result is generated and output.

[0069] In some specific examples, structural elements include but are not limited to key visual features such as text, graphics, and color distribution.

[0070] In some specific examples, the differences include, but are not limited to, spots or blurred areas caused by excessive or uneven ink, differences in text content or font style, certain parts not printed correctly or missing entirely, color performance not meeting expected standards, etc.

[0071] In some specific examples, the online proofing result can be an online proofing report, which lists all the problems found and their locations, and provides corresponding suggestions. The report content may include: problem description: detailed description of the specific situation of each difference point; location information: indicating the specific location coordinates of the problem in the image; correction suggestions: proposing possible solutions for each problem, such as adjusting the printing plate, resetting parameters, etc.

[0072] In some specific examples, the generated online proofing results can be transmitted to the human-computer interaction module in real time through an internal network or a dedicated communication interface. The human-computer interaction module enables operators to intuitively view and manage the operating status of the system. In some specific examples, the online proofing results can be displayed and processed through a touch screen display, which can display the online proofing results in a graphical interface, including visual annotations of difference points, location information, and problem descriptions; operators can directly click or slide on the screen to enlarge the image, select specific areas for detailed inspection, and even mark or annotate certain problems. The touch screen display can provide a series of shortcut buttons for quick access to common operations, such as re-testing, adjusting settings, etc.

[0073] S300, when the online proofing result is qualified, the online modeling result is used as the detection standard to carry out continuous printing production.

[0074] When the operator determines that the online proofing results are qualified, the operator issues a printing control instruction to the printing control module through the human-computer interaction module. The printing control module will use the previously established online modeling results as the quality standard for subsequent printing production and carry out continuous batch production. All subsequent prints will be compared with the standard template obtained by online modeling to ensure the quality consistency of subsequent products.

[0075] In some embodiments, the operator reviews the online proofreading results. If the online proofreading results are unqualified, appropriate measures need to be taken to correct them and then the above process needs to be repeated until they are qualified.

[0076] The corresponding measures can usually be to ask the operator to recheck whether there are errors in the electronic files and printing plates, check whether there are errors in the printed image acquisition process, check whether there are errors in the pre-processing of the electronic files, etc., and repeat the above steps after troubleshooting and correcting the errors.

[0077] The above process is repeated, including re-capturing the first print image, calibrating, registering, comparing and evaluating, until a satisfactory result is obtained.

[0078] One or more of the above embodiments of the present application provide an online detection method with the function of first proofing of printed products. By executing the online modeling and online proofing steps of the first printed product in parallel, it is possible to achieve instant detection and feedback, and the time on the production line is fully utilized, reducing the production stagnation caused by waiting for the proofing results, and greatly improving the work efficiency of the first proofing. And by calibrating, registering, comparing and evaluating the image of the first printed product, it is possible to automatically identify and correct the image deviation caused by paper jitter and slight deformation, and ensure the accurate match between the image and the electronic file. In addition, the degree of automation of the online detection system is significantly improved, reducing the need for manual intervention. In traditional methods, manual proofing is often prone to omissions and errors, while the method provided by the present application greatly reduces the possibility of human errors through automated proofing and modeling processing, ensuring that each step of the operation can be strictly performed in accordance with the standard.

[0079] Figure 3 It is a structural schematic diagram of an online detection system with a first proofreading function of printed matter provided by an exemplary embodiment of the present application; Figure 4 It is a structural schematic diagram of a first proofing module of an online detection system with a first proofing function for printed products provided by an exemplary embodiment of the present application.

[0080] See also Figure 3 and Figure 4 In a second aspect, the present application provides an online detection system with a first proofreading function for printed products, which is used for online detection of printing production under the control of a printing control module 100, and includes an image acquisition module 200, an online modeling module 300, a first proofreading module 400, a human-computer interaction module 500 and an online monitoring module 600.

[0081] The image acquisition module 200 is the first link of the online detection system, and its main function is to acquire the image of the first printed product produced on the printing press in real time through a high-resolution camera or sensor.

[0082] In some specific examples, the image acquisition device 200 may be a CCD camera (eg, an industrial camera of model XG-5000) or a line scanner.

[0083] The image acquisition module 4200 ensures that the acquired image has a sufficiently high resolution (usually above 300 dpi) to ensure the accuracy of subsequent calibration and modeling.

[0084] The online modeling module 300 is connected to the image acquisition module 200 to receive the first printed product image and perform modeling processing in real time. The modeling processing is to generate a standard template based on the first printed product image collected. This template will be used as a standard for printed product quality inspection in the subsequent production process.

[0085] In some specific examples, the online modeling module 300 generally includes a computer vision algorithm and a processing unit (such as a CPU, a GPU) to support a modeling function based on image analysis. Commonly used image processing algorithms include edge detection, feature matching, color correction, and the like.

[0086] The online modeling module 300 can construct an accurate digital inspection template by extracting key information (such as text, color, and graphic elements) from the image. The inspection template improves the quality control efficiency during the production process and ensures the consistency of quality of each printed product.

[0087] The first proofing module 400 is connected to the image acquisition module 200 and the printing control module 100, and its main function is to perform proofing checks based on the first printed product image acquired. It can receive the electronic sample file from the printing control module 100 and compare it with the first printed product image actually acquired, and automatically detect the difference between the first printed product and the design requirements. The first proofing module 400 can find and mark problems such as ink spots, character mismatches, color deviations, etc., and pass the results to subsequent modules for processing.

[0088] In some specific examples, the first proofing module 400 may include an image processing unit (such as a GPU accelerated processor) and a proofing software system that can automatically read the electronic file of the printing plate (such as a PDF file or other design file) and compare it with the image.

[0089] The First Proof Module 400 can significantly reduce the workload of manual inspection and ensure that possible quality problems are discovered before printing production begins. It can provide real-time feedback on proofing results and provide detailed defect reports to operators. Through automated proofing, production efficiency can be effectively improved, human oversight can be reduced, and consistent quality standards can be ensured for each batch of printed products.

[0090] The human-machine interaction module 500 (HMI) is connected to the first proofing module 400 and the printing control module 100. Its main function is to display the online proofing results to the operator through a visual interface and issue control instructions to the printing control module 100 according to the proofing results. The human-machine interaction module 500 not only displays the image comparison results, but also allows the operator to confirm or modify the proofing results and adjust the production parameters in time.

[0091] In some specific examples, the human-computer interaction module 500 is generally composed of a touch screen, an operation panel, an industrial computer or a PC. The touch screen can display real-time proofing data, image comparison results and other important parameters, such as color difference, defective area, etc. Through the interface, the operator can view real-time data, receive alarms and make operational adjustments.

[0092] The human-machine interaction module 500 provides a user-friendly interface that enables operators to track proofing results in real time, promptly identify problems in production, and adjust equipment settings according to system prompts. This module effectively improves operational convenience, reduces operating errors, and enhances visibility and controllability of the production process.

[0093] The online inspection module 600 is connected to the online modeling module 300. Its main function is to use the online modeling results as the inspection standard to perform real-time quality inspection on the continuous printing production process. This module can automatically inspect each subsequent batch of printed products and ensure that they meet the preset quality standards. The online inspection module will automatically compare the images based on the modeling results. If any non-compliant areas are found (such as pattern misalignment, color deviation, missing images, etc.), it will issue an alarm, mark errors, and other operations.

[0094] In some specific examples, the online detection module 600 is generally composed of an image processing unit, a sensor, and an analysis algorithm, which can automatically scan the printed product and compare the template. The detection algorithm can include a machine learning model that can adapt to different printing modes and quality standards.

[0095] The online inspection module 600 ensures that each product meets quality standards by monitoring the quality of each batch of printed products in real time. It can detect quality problems in time and prevent the production of a large number of defective products. This module not only improves the automation level of the production line, but also reduces the need for manual quality inspection, improving production efficiency and the accuracy of quality control.

[0096] In some specific examples, the printing control module 100 can receive instructions from the human-computer interaction module 500, such as controlling the printing device to print or stop printing, adjusting color configuration, resolution, paper type, etc., and can also transmit the electronic file of the printing plate to the first proofing module 400 for proofreading.

[0097] In some embodiments, see Figure 3 The first proofing module 400 includes: a calibration unit 410, a registration unit 420 and an evaluation unit 430.

[0098] The calibration unit 410 is connected to the image acquisition module 200 and the printing control module 100, and is used to receive the first printed product image and the printing plate electronic file, and calibrate the first printed product image according to the printing plate electronic file.

[0099] In some specific examples, the operations performed by the calibration unit 410 include: The electronic file of the printing plate is preprocessed to obtain an electronic file comparison image of a set resolution: the electronic file of the printing plate is converted into a first image of a set resolution; the pattern and background of the first image are segmented and blurred; the pattern texture of the first image is extracted and merged according to the three RGB color channels, and a texture mask image is generated through a threshold value to obtain an electronic file comparison image of a set resolution.

[0100] Adjust the resolution of the first printed image to make it consistent with the resolution of the electronic file reference image with the set resolution; Perform geometric correction on the first print image: select the coarse corner points on the first print image through an algorithm, and further select the fine corner points as the calibration basic nodes; perform similarity comparison and correction restoration with the electronic file reference image within the scope of each calibration basic node to restore the local deformation of the first print image to the greatest extent; Perform color correction on the first print image to further reduce the difference between the first print image and the electronic file comparison image; The first printed image is filtered to make the blur degree of the first printed image nearly consistent with the blur degree of the electronic file comparison image.

[0101] The registration unit 420 is connected to the calibration unit 410 and is used to combine the calibrated first printed product image and register the printing plate electronic file and the calibrated first printed product image.

[0102] In some specific examples, the operations performed by the registration unit 420 include: feature point recognition: using image processing algorithms (such as SIFT, SURF or ORB) to automatically detect and extract significant feature points in the electronic file of the printing plate. These feature points can be the corners of the pattern, the edges of the text, the intersections of the graphics, etc.; positioning kernel selection: from the large number of extracted feature points, through a series of screening rules (such as stability, repeatability, etc.), select the most representative and reliable points as "positioning kernels"; matching positioning kernels: for each positioning kernel, find its corresponding best matching point in the calibrated first print image, so as to establish multiple position correspondences; multi-region mapping: through the matching of a large number of positioning kernels, realize the mapping of multiple regions between the electronic file of the printing plate and the calibrated first print image to ensure global consistency; local mapping optimization: weighted processing of local mapping relationships, optimize matching accuracy, obtain accurate mapping relationships between various positions between images, and ensure the consistency of the overall structure.

[0103] The evaluation unit 430 is connected to the registration unit 420 and the human-computer interaction module 500 respectively, and is used to compare and evaluate the registered image structure of the first printed product with the electronic file of the printing plate, and output the online proofreading result to the human-computer interaction module 500.

[0104] In some specific examples, the operations performed by the evaluation unit 430 include: one-by-one comparison: based on the position correspondence after registration, the image structure of the first printed product is compared with the structural elements of the electronic file of the printing plate one by one to find the difference points. These structural elements include but are not limited to key visual features such as text, graphics, color distribution, etc.; difference detection: identifying and marking any inconsistencies between the two images, such as dirty ink, inconsistent characters, missing images and text, color deviation, etc.; preset evaluation method: according to the pre-set evaluation criteria (such as tolerance range, threshold, etc.), the found difference points are screened to distinguish between real problems and small errors that can be ignored; priority sorting: the screened difference points are prioritized according to the severity to ensure that the most important problems are paid attention to and handled first; report generation: automatically generate a detailed online proofing report and feed it back to the human-computer interaction module 500, listing all the problems found and their locations, and providing corresponding suggestions. The report content may include problem description, location information, correction suggestions, etc.

[0105] It should be noted that the technical solutions in the various embodiments of the present application can be combined with each other, but the basis for the mutual combination is that it can be implemented by ordinary technicians in the field; when the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist, that is, it does not belong to the scope of protection of this application.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An online detection method with the function of first proofreading of printed matter, characterized in that: include: Capture the first print image while printing the first print; Online modeling based on first print images; Online proofing based on first print images; When the online proofing results are qualified, the online modeling results are used as the testing standard for continuous printing production; Among them, online modeling based on the first print image and online proofing based on the first print image are carried out simultaneously; Online proofing based on first print images includes: Preprocess the electronic file of the printing plate to obtain a comparison image of the electronic file with a set resolution; Calibrate the first print image against the electronic file reference image; Register the electronic file reference image and the calibrated first print image; The image structure of the first printed product after registration is compared and evaluated with the reference image of the electronic file, and the online proofreading result is output.

2. The online detection method with the function of first proofreading of printed matter according to claim 1, characterized in that: Preprocess the electronic file of the printing plate to obtain the electronic file comparison image of the set resolution, including: Converting the electronic file of the printing plate into a first image of a set resolution; Segmenting the first image into pattern and background, and performing blur processing; The pattern texture of the first image is extracted and merged according to the three color channels of RGB, and a texture mask image is generated through a threshold value to obtain an electronic file comparison image of a set resolution.

3. The online detection method with the function of first proofreading of printed matter according to claim 1, characterized in that: Calibration of the first print image against the electronic file comparison image includes: Adjust the resolution of the first printed image to make it consistent with the resolution of the electronic file reference image with the set resolution; Performing geometric correction on the first print image to restore local deformation of the first print image; Perform color correction on the first print image to further reduce the difference between the first print image and the electronic file comparison image; The first printed image is filtered to make the blur degree of the first printed image nearly consistent with the blur degree of the electronic file comparison image.

4. The online detection method with the function of first proofreading of printed matter according to claim 3, characterized in that: The geometric correction of the first print image includes: A plurality of coarse corner points are obtained on the first printed image by using a corner detection algorithm; fine corner points are screened out from the coarse corner points by using a clustering algorithm; a similarity comparison is performed between the fine corner points and the electronic file control image in the corresponding area based on the fine corner points; and the position, rotation and scaling of the first printed image are adjusted according to the comparison results to correct local deformation.

5. The online detection method with the function of first proofreading of printed matter according to claim 1, characterized in that: Registration of the electronic file reference image and the calibrated first print image includes: Detect and extract significant feature points in electronic document comparison images; Select the positioning kernel from the feature points; Determine the best matching point corresponding to each positioning kernel in the first print image to establish a position correspondence; Mapping of multiple areas between the electronic file of the printing plate and the first print image is achieved based on matching of a large number of positioning kernels; The local mapping relationship is weighted to obtain the accurate mapping relationship between each position of the images.

6. The online detection method with the function of first proofreading of printed matter according to claim 1, characterized in that: Compare and evaluate the image structure of the first printed product after registration with the electronic file reference image, and output online proofing results including: Based on the position correspondence after registration, the structural elements of the first printed image and the electronic file reference image are compared one by one to find out the differences; According to the preset evaluation method, the difference points are screened and the online proofreading results are generated.

7. The online detection method with the function of first proofreading of printed matter according to claim 1, characterized in that: Compare and evaluate the image structure of the first printed product after registration with the reference image of the electronic file. After outputting the online proofing results, the following also includes: The online proofreading results are displayed through the human-computer interaction module. The operator reviews the online proofreading results. If the online proofreading results are unqualified, appropriate measures must be taken to correct them and then repeat the calibration, registration and comparison operations until they are qualified.

8. An online inspection system with the function of first proofing printed matter, used for online inspection of printing production under the control of a printing control module, characterized in that: include: An image acquisition module, used for acquiring the first printed product image; An online modeling module, connected to the image acquisition module, and used for performing online modeling according to the first printed product image; A first proofing module, connected to the image acquisition module and the printing control module, for performing online proofing according to the first printed product image; A human-computer interaction module, connected to the first proofing module and the printing control module, for displaying the online proofing result and issuing a control instruction to the printing device according to the online proofing result; The online detection module is connected to the online modeling module and is used to detect the printed products produced by continuous printing by taking the online modeling results as the detection standard.

9. The online inspection system with the function of first proofreading of printed matter according to claim 8, characterized in that: The first proofing module includes: A calibration unit, connected to the image acquisition module and the printing control module, for receiving the first printed product image and the printing plate electronic file, preprocessing the printing plate electronic file to obtain an electronic file comparison image of a set resolution, and calibrating the first printed product image according to the electronic file comparison image; A registration unit connected to the calibration unit, for combining the calibrated first printed product image and registering the electronic file comparison image with the calibrated first printed product image; The evaluation unit is connected to the registration unit and the human-computer interaction module respectively, and is used to compare and evaluate the image structure of the first printed product after registration with the electronic file comparison image, and output the online proofreading result to the human-computer interaction module.

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